def _compute_svmnormalvector((cache_dir, images, control_images,
normalization_name, preprocess_file, rfe)):
#try:
import numpy as np
import sys
from cpa.profiling.cache import Cache, RobustLinearNormalization, normalizations
from sklearn.svm import LinearSVC
from cpa.profiling.profile_svmnormalvector import _compute_rfe
cache = Cache(cache_dir)
normalization = normalizations[normalization_name]
normalizeddata, normalized_colnames, _ = cache.load(images, normalization=normalization)
control_data, control_colnames, _ = cache.load(control_images, normalization=normalization)
if preprocess_file:
preprocessor = cpa.util.unpickle1(preprocess_file)
normalizeddata = preprocessor(normalizeddata)
control_data = preprocessor(control_data)
assert len(control_data) >= len(normalizeddata)
downsampled = control_data[np.random.randint(0, len(control_data), len(normalizeddata)), :]
x = np.vstack((normalizeddata, downsampled))
y = np.array([1] * len(normalizeddata) + [0] * len(downsampled))
clf = LinearSVC(C=1.0)
m = clf.fit(x, y)
normal_vector = m.coef_[0]
if rfe:
# Copy because it is immutable (normal_vector.flags.weriteable == False)
normal_vector = np.array(normal_vector)
normal_vector[~_compute_rfe(x, y)] = 0
return normal_vector
def _compute_svmnormalvector(xxx_todo_changeme):
#try:
(cache_dir, images, control_images, normalization_name, preprocess_file,
rfe) = xxx_todo_changeme
import numpy as np
import sys
from cpa.profiling.cache import Cache
from cpa.profiling.normalization import RobustLinearNormalization, normalizations
from sklearn.svm import LinearSVC
from cpa.profiling.profile_svmnormalvector import _compute_rfe
cache = Cache(cache_dir)
normalization = normalizations[normalization_name]
normalizeddata, normalized_colnames, _ = cache.load(
images, normalization=normalization)
control_data, control_colnames, _ = cache.load(control_images,
normalization=normalization)
if preprocess_file:
preprocessor = cpa.util.unpickle1(preprocess_file)
normalizeddata = preprocessor(normalizeddata)
control_data = preprocessor(control_data)
assert len(control_data) >= len(normalizeddata)
downsampled = control_data[
np.random.randint(0, len(control_data), len(normalizeddata)), :]
x = np.vstack((normalizeddata, downsampled))
y = np.array([1] * len(normalizeddata) + [0] * len(downsampled))
clf = LinearSVC(C=1.0)
m = clf.fit(x, y)
normal_vector = m.coef_[0]
if rfe:
# Copy because it is immutable (normal_vector.flags.weriteable == False)
normal_vector = np.array(normal_vector)
normal_vector[~_compute_rfe(x, y)] = 0
return normal_vector
def _compute_svmnormalvector((cache_dir, images, control_images, rfe)):
# try:
import numpy as np
import sys
from cpa.profiling.cache import Cache, RobustLinearNormalization
from sklearn.svm import LinearSVC
from cpa.profiling.profile_svmnormalvector import _compute_rfe
cache = Cache(cache_dir)
normalizeddata, normalized_colnames, _ = cache.load(images, normalization=RobustLinearNormalization)
control_data, control_colnames, _ = cache.load(control_images, normalization=RobustLinearNormalization)
assert len(control_data) >= len(normalizeddata)
downsampled = control_data[np.random.randint(0, len(control_data), len(normalizeddata)), :]
x = np.vstack((normalizeddata, downsampled))
y = np.array([1] * len(normalizeddata) + [0] * len(downsampled))
clf = LinearSVC(C=1.0)
m = clf.fit(x, y)
normal_vector = m.coef_[0]
if rfe:
normal_vector[~_compute_rfe(x, y)] = 0
return normal_vector
